Biodemography of Exceptional Longevity in the United States - funded by the National Institute on Aging - investigates why some people manage to survive to extreme old age (100+ years) and what are the biological and social correlates of exceptional longevity with particular emphasis on the early-life events and conditions.

The purpose of this research project is to explore biological and social correlates of exceptional longevity with focus on long-term effects of early-life and mid-life events and conditions. The project will compile and validate a large family-linked dataset (over 2,000 records) taken from online family histories for persons with exceptional longevity (aged 100 and over) born in the United States in 1890-1895 and collect information about their childhood and adulthood living conditions using linkage to early U.S. censuses. In addition to this information, data on male physical characteristics (height and build) at the time when centenarians were young adults are also collected using data available in the WWI draft registration cards. In order to study effects of early childhood and adulthood conditions on longevity, it is important to compare long-lived individuals to a proper control group of shorter-lived persons. These persons are selected from the same pool of computerized family histories that was used for collection of long-lived individuals. Persons born in the same birth year window as centenarians but died at age 65 are selected from online family histories using web automation technique and used as controls in this study. As a result, a large-scale, user-friendly database with integrated, matched information on all potential predictors of exceptional longevity collected in this project will be developed and made available online for benefit of the research community. The project will investigate biological and social correlates of exceptional longevity in the United States with particular focus on early-life & mid-life events and conditions that favor survival to extreme old age.

The project takes advantage of the ongoing revolution in information technology for human longevity studies to examine the determinants of exceptional survival both on individual and population levels, using a rich variety of the U.S. data sources available through the Internet, including Social Security Administration datasets, Census data, family reconstitutions and validated genealogies, and military draft records.

One direction of research for this project is to explore why centenarians are different from their shorter-lived siblings who share genetic background and early-life living conditions. using a within-family approach. The project applied a within-family approach to study this issue and found significant beneficial effects of a young maternal age at a person's birth on survival to age 100 with particularly strong positive influence at a maternal age of 20-24 years. The effect of a young mother was particularly prominent in smaller families, pertinent today because of the smaller average family size in contemporary population. The project also explored the effects of month of birth (a proxy for early-life environmental influences) on the chances of survival to age 100. The within-family analysis found that months of birth have significant long-lasting effect on survival to age 100: siblings born in September-November have higher odds to become centenarians compared to siblings born in March. These results support the idea of early-life programming of human aging and longevity. These results were featured in mass media including Los Angeles Times.

Accurate estimates of mortality at advanced ages are essential to improving forecasts of mortality and the population size of the oldest old age group. Common believe among demographers now is that mortality at advanced ages deviates (decelerates) from the exponential Gompertz law following the logistic model. Study of large single-year U.S. birth cohorts (conducted in this project) found that mortality deceleration at advanced ages is negligible up to the age of 106 years and that the Gompertz model fits mortality trajectories better than the logistic model. Earlier reports of mortality deceleration (deviation of mortality from the Gompertz law) at ages below 100 appear to be artifacts of mixing together several birth cohorts with different mortality levels and using cross-sectional instead of cohort data. Age exaggeration and crude assumptions applied to mortality estimates at advanced ages may also contribute to mortality underestimation at very advanced ages. Similar results of no mortality deceleration at advanced ages were obtained using a smaller sample of centenarian siblings born outside the selection window for centenarians. These results are important for mortality forecasting and population estimates at advanced ages.